PT  - JOURNAL ARTICLE
AU  - Yiyi Yu
AU  - Riichiro Hira
AU  - Jeffrey N. Stirman
AU  - Waylin Yu
AU  - Ikuko T. Smith
AU  - Spencer L. Smith
TI  - Mice use robust and common strategies to discriminate natural scenes
AID  - 10.1101/156653
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 156653
4099  - http://biorxiv.org/content/early/2017/06/27/156653.short
4100  - http://biorxiv.org/content/early/2017/06/27/156653.full
AB  - Visual perception is essential for animal survival in natural environments. Mice use vision to navigate and avoid predators. However, the spatial resolution of mouse vision is poor compared to primates and carnivores, and mice lack a fovea. Thus, it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system, which measured the discriminability for sets of target and distractor scenes. We found that a conventional metric for estimating image resemblance, structural similarity (SSIM), reasonably predicted the discrimination performance. Mouse-to-mouse consistency was high, and the performance of each mouse was better predicted by the population mean than SSIM. This high intermouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several alternative image metrics to find an alternative to SSIM for predicting mouse discrimination performance. We found that a primary visual cortex (V1) model-inspired approach predicted mouse performance with fidelity comparable to the inter-mouse agreement. The model was based on convolving the images with Gabor filters, and its performance was orientation-specific. The orientation-specificity was stimulus-dependent. These results indicate that, compared to artificial parameters like SSIM, neurophysiological-based models of V1 processing can better predict visual discrimination behavior of naturalistic stimuli.